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Austerity and Anarchy: Budget Cuts and Social Unrest in Europe, 1919-2009

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Social unrest has led to key turning points in modern history since, at least, the French Revolution. Marx saw it as the driving force of the transition of societies from feudalism to capitalism and, eventually, communism. Unrest’s power as a catalyst for change manifests itself explicitly regime changes, such as during the “Arab Spring” of 2010-2011, or it operates through expectations: The extension of the franchise in Western societies has been interpreted as an attempt to heed off the threat of revolution (Acemoglu and Robinson 2000). What leads to social unrest is less clear. Economic shocks are one important contributing factor: The demise of the Weimar Republic during the Great Depression is a prominent example of how economic hardship can translate into unrest (Bracher 1978).

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									                DISCUSSION PAPER SERIES



                                      No. 8513

                       AUSTERITY AND ANARCHY: BUDGET
                         CUTS AND SOCIAL UNREST IN
                              EUROPE, 1919-2009


                       Jacopo Ponticelli and Hans-Joachim Voth


                        INTERNATIONAL MACROECONOMICS




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Available online at:                          www.cepr.org/pubs/dps/DP8513.asp
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                                                                     ISSN 0265-8003


AUSTERITY AND ANARCHY: BUDGET CUTS AND
   SOCIAL UNREST IN EUROPE, 1919-2009
              Jacopo Ponticelli, Universitat Pompeu Fabra
            Hans-Joachim Voth, UPF-ICREA, CREI and CEPR

                         Discussion Paper No. 8513
                                August 2011

                      Centre for Economic Policy Research
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 Economic Policy Research. Research disseminated by CEPR may include
 views on policy, but the Centre itself takes no institutional policy positions.

 The Centre for Economic Policy Research was established in 1983 as an
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 of open economies and the relations among them. It is pluralist and non-
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 These Discussion Papers often represent preliminary or incomplete work,
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 paper should take account of its provisional character.

 Copyright: Jacopo Ponticelli and Hans-Joachim Voth
CEPR Discussion Paper No. 8513

August 2011


                                            ABSTRACT

  Austerity and Anarchy: Budget Cuts and Social Unrest in Europe,
                            1919-2009*

Does fiscal consolidation lead to social unrest? From the end of the Weimar
Republic in Germany in the 1930s to anti-government demonstrations in
Greece in 2010-11, austerity has tended to go hand in hand with politically
motivated violence and social instability. In this paper, we assemble cross-
country evidence for the period 1919 to the present, and examine the extent to
which societies become unstable after budget cuts. The results show a clear
positive correlation between fiscal retrenchment and instability. We test if the
relationship simply reflects economic downturns, and conclude that this is not
the key factor. We also analyse interactions with various economic and
political variables. While autocracies and democracies show a broadly similar
responses to budget cuts, countries with more constraints on the executive
are less likely to see unrest as a result of austerity measures. Growing media
penetration does not lead to a stronger effect of cut-backs on the level of
unrest.

JEL Classification: H40, H50, H60 and N14
Keywords: demonstrations, Europe, government deficits, instability, public
expediture, riots and unrest

Jacopo Ponticelli                                      Hans-Joachim Voth
Department of Economics                                Department of Economics
Universitat Pompeu Fabra                               Universitat Pompeu Fabra
Ramon Trias Fargas, 25-27                              Ramon Trias Fargas 25-27
08005 - Barcelona                                      08005 Barcelona
SPAIN                                                  SPAIN

Email: jacopo.ponticelli@upf.edu                       Email: jvoth@crei.cat

For further Discussion Papers by this author see:      For further Discussion Papers by this author see:
www.cepr.org/pubs/new-dps/dplist.asp?authorid=174068   www.cepr.org/pubs/new-dps/dplist.asp?authorid=140053


* We thank Jordi Galí for inspiring this work. Financial support by the
European Research Council and the Barcelona GSE is gratefully
acknowledged.

Submitted 20 July 2011
                                                                                                    2

1. Introduction
Social unrest has led to key turning points in modern history since, at least,
the French Revolution. Marx saw it as the driving force of the transition of
societies from feudalism to capitalism and, eventually, communism. Unrest’s
power as a catalyst for change manifests itself explicitly regime changes, such
as during the “Arab Spring” of 2010-2011, or it operates through expectations:
The extension of the franchise in Western societies has been interpreted as an
attempt to heed off the threat of revolution (Acemoglu and Robinson 2000).1
What leads to social unrest is less clear. Economic shocks are one important
contributing factor: The demise of the Weimar Republic during the Great
Depression is a prominent example of how economic hardship can translate
into unrest (Bracher 1978).2
        In this paper, we examine what leads to social instability and violent
protests. In particular, we ask whether fiscal policy affect the level of social
unrest. The extent to which societies fracture and become unstable in response
to drastic changes in the government budget is a primary concern for
policymakers attempting to reduce budget deficits: From Argentina in 2001
to Greece in 2010-11, austerity measures have often created a wave of violent
protests and massive civil unrest. Economic conditions can deteriorate further
and faster if political and social chaos follows attempts to reign in spending.
Consequently, sustainable debt levels for countries that are prone to unrest
may be lower than they otherwise would be.
        We use a long panel dataset covering almost a century, focusing on
Europe, 1919 to 2009. The continent went from high levels of instability in the
first half of the 20th century to relatively low ones in the second, and from
frequently troubled economic conditions to prosperity. It thus provides a rich
laboratory of changing economic, social and political conditions. In terms of
outcome variables, we focus on riots, demonstrations, political assassinations,
government crises, and attempted revolutions. These span the full range of
forms of unrest, from relatively minor disturbances to armed attempts to
overthrow the established political order. We compile a new index that
summarizes these variables, and then ask -- for every percentage cut in
government spending, how much more instability should we expect?
        The data shows a clear link between the magnitude of expenditure cut-
backs and increases in social unrest. With every additional percentage point of

1
  In a related exercise, Boix (2003) models the incentives of the populace to resort to violence as a
function of the wealth distribution and economic development.
2
  The French Revolution has also been interpreted in these terms (Soboul 1974; Doyle 2001). The view
is controversial (Hunt 2004; Cobban 1964).
                                                                                                                             3

GDP in spending cuts, the risk of unrest increases. As a first pass at the data,
Figure 1 examines the relationship between fiscal adjustment episodes and the
number of incidents indicating instability (CHAOS). CHAOS is the sum of
demonstrations, riots, strikes, assassinations, and attempted revolutions in a
single year in each country. The first set of five bars show the frequencies
conditional on the size of budget cuts. When expenditure is increasing, the
average country-year unit of observation in our data registers less than 1.5
events. When expenditure cuts reach 1% or more of GDP, this grows to
nearly 2 events, a relative increase by almost a third compared to the periods
of budget expansion. As cuts intensify, the frequency of disturbances rises.
Once austerity measures involve expenditure reductions by 5% or more, there
are more than 3 events per year and country -- twice as many as in times of
expenditure increases.


                       3.5

                                                                                                expenditure increases
                                                                                                expenditure reduction >1%
                       3.0
                                                                                                >2%
                                                                                                >3%
                                                                                                >5%
                       2.5
 number of incidents




                       2.0




                       1.5




                       1.0




                       0.5




                       0.0
                             CHAOS   demonstrations            riots           assassinations              general strikes

                                                      Measure of instability

Figure 1: Frequency of incidents and the scale of expenditure cuts

Exactly the same relationship can be observed in each of the four main
subcategories of CHAOS. The frequency of demonstrations, assassinations,
and general strikes rises monotonically with the scale of cuts. Only in the case
of riots is there a small decline for the biggest cut-backs. In the case of
demonstrations, the frequency of incidents appears to rise particularly fast as
expenditure cuts pass the 3% threshold.
        The strength of the link between austerity measures and unrest is our
first important finding. Is the link causal? Other factors, such as generally
                                                                                 4

depressed economic conditions, could drive up unrest and the need for cut-
backs simultaneously. Controlling for economic growth does not change our
results. This suggests that we capture more than the general association
between economic downturns and unrest. To demonstrate that causality runs
from cut-backs to unrest, we refine the data in two ways: First, we analyse a
more detailed dataset that gives information about the causes of each
incident. Second, we use recently-compiled data on changes in the government
budget that follow directly from policy changes (Devries et al. 2011). For both
types of additional evidence, we find clear indications that the link runs from
budget cuts to unrest. We also conduct placebo tests with other types of
unrest – inspired by ecological issues and world peace, for example – and find
no effect of budget measures.
       Our findings are robust to a wide range of alternative specifications and
further tests. Different measures of unrest do not affect our conclusions. We
examine if the link between austerity and unrest changes as countries
institutions improve. For most value of the Polity2 score of institutional
quality, results are broadly unchanged. However, countries with very high
levels of constraints on the executive show a weaker degree of association.
Further, we examine if the spread of mass media changes the probability of
unrest. This is not the case. If anything, higher levels of media availability
and a more developed telecommunications infrastructure reduce the strength
of the mapping from budget cuts to instability. We also test which part of the
distribution of unrest is responsible for our results, using quantile regressions:
The higher the level of unrest, the bigger the relative impact of additional
budget cuts. Finally, we test for asymmetries in the relationship between
unrest and austerity. Reductions increase instability, but spending increases
do not cut the number of incidents to the same extent.
       Earlier papers on the same topic have typically focussed on case
studies, or on subsets of the developing world. Work on 23 African countries
during the 1980s found that budget cuts had typically no effect on political
and social stability. IMF interventions, on the other hand, often led to more
frequent disturbances (Morrison, Lafay, and Dessus 1994). Paldam (1993)
examines current account crises in seven South American countries during the
period 1981-90, using high-frequency (weekly) data. He finds that the run-up
to new austerity measures is associated with higher levels of unrest, but that
actual implementation is followed by fewer disturbances. Similarly, Haggard,
Lafay and Morrison (1995) find that IMF interventions and monetary
contractions in developing countries led to greater instability. Analysing the
                                                                                                     5

period 1937-1995, Voth (2011) explores related issues for the case of Latin
America. He finds that austerity and unrest are tightly linked in a majority of
cases. Remarkably, to the best of our knowledge, there exists no systematic
analysis of how budget cuts affect the level of social instability and unrest in a
broad cross-section of developed countries, over a long period.
        Other related literature includes work on the political economy of fiscal
consolidation, and on its economic effects. The composition of fiscal
adjustment has been examined; cutting entitlement programs tends to
produce persistent improvements in the budget balance, while revenue
measures and capital expenditure cuts have only temporary effects (Alesina
and Perotti 1995). The timing of stabilization measures has been explored in
war-of-attrition models, which view relative bargaining strength of different
groups as crucial (Alesina and Drazen 1991). A rich literature has examined
the macroeconomic effects of budget cuts. Giavazzi and Pagano (1990) and
Alesina et al. (2002) find that cuts can be expansionary. Amongst the reasons
suggested for this finding are a reduction in uncertainty about the course
future spending (Blanchard 1990a), and a positive wealth shock as a result of
lower taxes in the future (Bertola and Drazen 1993).3 Recently, work by the
IMF has suggested that austerity measures may be less expansionary than
previously thought; they may well have the standard negative Keynesian
effects as a result of lower demand (IMF 2010; Pescatori, Leigh, and Guajardo
2011).
        We proceed as follows: Section 2 presents our data, and section 3
summarizes our main results. Robustness checks and extensions are discussed
in section 4; section 5 concludes.


2. Data
In this section, we briefly describe our data and summarize its main features.
We use two datasets – a long-term one which allows tracing out the broad
patterns of unrest and austerity since 1919, as well as a short-term one that
contains richer information on the causes of unrest. For both, we use
information on unrest as well as on economic performance and budget
measures.
       Five main indicators of domestic conflict in the long-term data will
form the main focus of this study – general strikes, riots, anti-government
demonstrations, political assassinations, and attempted revolutions. These

3
 Once the response of labor supply and capital formation is fully taken into account, these effects may
not go through (Baxter and King 1993).
                                                                                                   6

data are part of the Cross National Time Series Dataset, compiled by Arthur
Banks (2010) and his collaborators. The main source of data on unrest
episodes are the reports of the The New York Times, while the variables’
definition is adopted from Rummel (1974). In addition, we use data on GDP,
government revenue, expenditure, and the budget balance from a variety of
sources.4 The long-term data has information on 26 European countries and
covers the years from 1919 to 2008. 5
        Table 1 gives an overview of the main variables and their descriptive
statistic for the long-term data. The average number of assassinations and
general strikes was quite low in our sample, with less than 2 events in each
decade. There were more riots and more demonstrations – 5-6 per decade.
Attempted revolutions are quite rare, but some countries registered high levels
of instability. The record in our sample is Germany in 1923, with 5 recorded
attempts at overthrow (with communist insurgencies in Saxony and
Thuringia, the Hitler Beer Hall Putsch, and a separatist movement in the
Rhineland). Assassinations and riots similarly show a broad range of observed
values.
        Using almost a century of data allows us to include some extreme
observations. For example, Austria and Germany saw major output declines
in 1945 and 1946, respectively. The biggest reduction in governments spending
in our data occurred in Poland, in 1982; the second-largest, in Finland, in
1947. The start of war is often associated with big increases in expenditure.
The record-holder in our dataset is Hungary in 1940, with an increase of over
30 percent.




4
  Data on fiscal variables (Total Central Government Expenditure and Revenue) and GDP are from
OECD Stat (2010) for years from 1970 onwards, and from Mitchell (2005) for the period 1919-1970.
Data on GDP growth in real terms for the all sample are from Maddison (2010).
5
  The 26 European countries included in the long-term data are: Austria, Belgium, Bosnia and
Herzegovina, Bulgaria, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy,
Netherlands, Norway, Poland, Portugal, Romania, Russian Federation, Serbia, Slovak Republic,
Slovenia, Spain, Sweden, Switzerland, United Kingdom, Yugoslavia.
                                                                                                         7

Table 1: Descriptive statistics, main variables




To obtain a single measure of instability, we calculate CHAOS by taking the
sum of the number of assassinations, demonstrations, riots, general strikes,
and attempted revolutions. While a crude way of aggregating indicators, it
turns out to be powerful.6 In the robustness section, we show that alternative
methods of reducing data complexity such as principal components analysis do
not change our results.
      For CHAOS, the average country in our sample registers 1.5 incidents
per year. Instability was not constant over time. The maximum is higher –
Italy in 1947 saw a total of 38 incidents, including 7 general strikes, 19 riots,
and 9 anti-government demonstrations. Figure 1 gives an overview of the
evolution over time, plotting the average of CHAOS as well as the maximum
number of incidents observed. While there is no clear-cut pattern over time,
some features emerge. The interwar period showed relatively high levels of
unrest, with an average of 2 incidents per year, compared to 1.4 in the post-
war period. The immediate post-World War II period, and the period form
1968 to 1994 also show unusually high levels of unrest. Comparatively
speaking, the years since 1994 have been unusually tranquil (average CHAOS
= 0.78)




6
 One alternative is the weighted conflict indicator (wci), as compiled by Banks (2010). It gives fixed
weights determined to different forms of unrest: Demonstrations have a weight of 200, while political
assassinations have a weight of 24.
                                                                                                       8


                                  40



                                  35



                                  30
    number of incidents (CHAOS)




                                  25



                                  20



                                  15



                                  10


                                                                                                 max
                                  5



                                  0                                                              mean
                                    19

                                    22

                                    25

                                    28

                                    31

                                    34

                                    37

                                    40

                                    43

                                    46

                                    49

                                    52

                                    55

                                    58

                                    61

                                    64

                                    67

                                    70

                                    73

                                    76

                                    79

                                    82

                                    85

                                    88

                                    91

                                    94

                                    97

                                    00

                                    03

                                    06

                                    09
                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  19

                                  20

                                  20

                                  20

                                  20
                                                   year



Figure 1: CHAOS over time


The short-term data on unrest is from the European Protest and Coercion
Database (EPCD) developed by Francisco (2000). The EPCD codes daily data
on all reported protest events occurred in 28 European countries between 1980
and 1995. The data is constructed using the full-text reports from more than
400 newspapers in the Lexis-Nexis database. We restrict our attention to the
same types of protest events covered in the long-term data: riots,
demonstrations, political assassinations, general strikes, and attempted
revolutions.7 The main advantage of the EPCD over the Arthur Banks’
database is that the former records the issue behind each protest, allowing us
to test the relationship between austerity and unrest in a very precise way,
even if only for a small subset of the overall dataset.
       There are relatively few protests that are caused by austerity measures.
At the same time, when they happen, they involve a large number of
participants – by far the largest number of protesters of any category, as
Table 2 illustrates. These protests tend to be relatively peaceful, with few
protesters arrested, injured or killed, and relatively few members of the
security forces involved.




7
 We only consider protest events whose number of participants is above 100 for riots and
demonstrations and above 1000 for general strikes (no threshold is used for assassinations and
attempted revolutions). These are the same threshold used in the Arthur Banks database.
                                                                             9

Table 2: Unrest in the EPCD sample, 1980-95.




In compiling information on expenditure and the budget balance data, we
need to trade off the accuracy of information against availability over a long
time span. For the 1919-2009 dataset, we rely on standard data sources on the
central government revenue and expenditure relative to GDP (Mitchell 2007)
for the years 1919 to 1970, augmented by data from the OECD (2010) for the
period thereafter.
       Expenditure changes will serve as the main explanatory variable.
Figure 2 graphs changes in expenditure/GDP from one year to the next. The
distribution is almost symmetric around the mean, with similar numbers of
country-years witnessing expenditure increases and declines (807 vs 685). In
an average year and country over the period, central government expenditure
relative to GDP rose by 0.3%. The vast majority of observations falls between
increases and decreases of 5%, with a few outliers in the tails of the
distribution (typically driven by the beginning and end of wars).
                                                                                               10




         25
         2015
     Density
         10
         5
         0




                -.2     -.1             0           .1              .2       .3
                              Expenditure/GDP, change from t-1 to t


Figure 2: Expenditure changes/GDP, 1919-2009, all countries


In addition, we use the data by Alesina and Ardagna (2010) for the cyclically-
adjusted budget balance.8 This has the advantage of correcting the budget
position for changes in interest payments and for the immediate effect of the
economic cycle, which drives both expenditure and revenue without any
additional policy decision being taken. For a subsample of the data (1978-
2009, 17 countries), we also use data by Devries et al. (2011). These authors
examine in detail the policy changes that led to changes in a country’s fiscal
stance. Only expenditure cuts or revenue increases motivated by a decision to
press ahead with fiscal consolidation are considered.9 Overall, Devries et al.
(2011) find 173 periods of fiscal policy adjustment,
       As a first pass at the data, we repeat the exercise in Figure 1 for
output growth (Figure 4). We subdivide the sample into terciles, and examine
how much the incidence of various indicators of unrest declines as growth
accelerates. For the summary indicator (CHAOS), there are a little more than
2 incidents when growth is in the lowest tercile. This falls to 1.3-1.5 incidents
as growth accelerates. There is also a clear pattern of decline for
demonstrations and for assassinations. In the case of riots, the differences are
smaller overall, whereas in the case of general strikes, there seems to be little
pattern at all. Based on a first, visual inspection of the data, it seems that the
link between budget cuts and unrest is clearer than the one with growth.


8
    Alesina and Ardagna use the method of Blanchard (1990b).
9
    The approach is similar to the “narrative approach” pioneered by Romer and Romer (1989).
                                                                                                                     11


                       2.5



                                                                         lowest tercile (median growth -0.5%)

                       2.0                                               middle tercile (2.7%)

                                                                         highest tercile (5.7%)
 number of incidents




                       1.5




                       1.0




                       0.5




                       0.0
                             CHAOS   demonstrations            riots           assassinations      general strikes

                                                      Measure of instability

Figure 4: Frequency of incidents and economic growth


Next, we examine the correlation structure of our data in Table 3.
Assassinations, general strikes, riots, revolutions and demonstrations are all
positively and significantly correlated with each other. This supports our
assumption that they reflect a broader underlying pattern of social instability
and unrest. CHAOS is also positively correlated with the weighted conflict
index (wci). Finally, Table 3 suggests that higher levels of expenditure and
faster growth are associated with less unrest. The simple correlation of
CHAOS with changes in the budget balance is positive and significant. Higher
taxes and lower expenditure are associated with more unrest, but the
relationship is not significant.
                                                                                12

Table 3: Correlation matrix, main variables




(significance levels in parentheses)


In the case of output changes, the coefficient is negative, but insignificant
(table 3). The simple correlations suggest that these co-movements do not
extend to all indicators of unrest equally – riots, revolutions, and
demonstrations decline as expenditure rises, but assassinations and strikes
seem – at a first pass – uncorrelated. Similarly, output growth seems to
correlate negatively with assassinations, riots, revolutions, and
demonstrations, but not with strikes. Next, we examine the connection
between budget position, expenditure, and unrest more systematically.


3. Results


The graphical evidence in Figures 1 and 4 suggests a link from “hard times” –
low growth and budget cut-backs – to unrest. Next, we examine if there is a
systematic relationship between budget measures and social instability. In this
section we also address the issue of causality, while in the next section we
will test the robustness of our results.


A. Baseline Results
We estimate panel regressions of the type:
I it   i  t  Bit  X 'it   it                         (1)
                                                                                                    13

where Iit denotes the level of instability in country i at time t, B is an
indicator of the change in the budget position,  is a country-specific
intercept,  is a time-specific dummy, and X’ is a vector of control variables.
       We use CHAOS as the dependent variable in our baseline specification,
and test the robustness of findings to alternative specifications later. Table 4
gives the main results. Under OLS with fixed effects and year-dummies, we
find that expenditure increases reduce instability in a powerful way (column
1). A one standard-deviation increase in expenditure cuts the number of
incidents (CHAOS) by 0.4 per year and country. Tax increases have a positive
sign, but the effect is not significant at standard levels of rejection (column 2).
It is also small – a one standard deviation rise in the tax/GDP ratio increases
unrest by less than 0.01 events. Overall, we find that improvements in the
budget balance raise the level of unrest (column 3). As the results in columns
(1) and (2) make clear, this reflects the impact of expenditure cuts, and not of
tax increases.
       CHAOS is a count variable. Hence, the use of OLS may not be
appropriate. Columns (4)-(6) give the results for Poisson Quasi-Maximum
Likelihood estimation, with fixed effects. We find the same pattern as before,
with strong effects for expenditure cuts and much weaker ones for tax
increases.10




10
     We also experimented with using negative binomial regressions, but results were largely unchanged.
                                                                               14

Table 4: Baseline results




Which component of CHAOS is responsible for the significant predictive
power of budget cuts? In Table 5, we use the same specification as in Table 4
under Poisson QML, looking at the effect of expenditure cuts on each of the
components of the aggregate indicator of instability – general strikes,
demonstrations, riots, assassinations, and attempted revolutions. Out of the
five outcome variables, four show the expected sign, and all of them are
statistically significant. The only variable that does not show a large,
significant coefficient is general strikes. On average, years with expenditure
increases showed fewer general strikes, but there are numerous general strikes
that are not an immediate reaction to economic conditions and budget
measures (such as, for example, the 1926 general strike in Britain). For the
other variables, the coefficients are large, indicating that austerity measures
coincide with significant increases in demonstrations, attempted revolutions,
riots, and assassinations.
        In all specifications, the effect of GDP growth on unrest is negative. In
contrast to the results for expenditure changes, the effect is not tightly
estimated, except in the case of demonstrations, when it is also large – every
1% increase in GDP cuts the number of demonstrations by close to 0.4 events.
                                                                               15

Table 5: Fiscal Adjustment and CHAOS by component




Table 6 takes this analysis one step further, by breaking the period 1919-2009
into four sub-periods. We distinguish the interwar period from the period of
immediate post-World War II reconstruction, the period of slowing growth
into the 1980s, as well as the years after the fall of the Berlin Wall after 1989.
On the whole, we find the same pattern as in the sample as a whole, with the
exception of the last two decades. The effect of changes in budget expenditure
on unrest is strongest in the tumultuous interwar years, when the estimated
coefficient is fifty percent larger than in the sample as a whole. The effect of
GDP growth is negative, but not tightly estimated. In the years after 1945,
the inverse relationship between expenditure and unrest remains. Strikingly,
however, more growth now appears to lead to more unrest. While it is difficult
to test for the causes of this reversal exactly, it seems that high rates of
output growth may have encouraged worker militancy more generally. At a
time when many countries reached full employment, this effect seems to have
become dominant. The normal pattern of GDP growth reducing unrest
reasserts itself after 1965, when there is also still a clear negative effect of
higher government expenditure.
        The fall of the Berlin wall saw the spread of Western-style democracy
eastwards. The overall connection between austerity and social instability now
changes sign, and becomes in insignificant. This suggests to us that non-
economic causes became a dominant feature of the period. Below, we examine
the issue in more detail with the help of a dataset that allows us to look at
the motive of each demonstration.
                                                                               16

Table 6: Results by sub-period and sub-sample




B. Causality
The obvious challenge in interpreting (1) is the potential for omitted variable
problems. It is possible that the economic cycle is simultaneously driving both
unrest and the need for budget cuts. Above, we already control for GDP
growth rates, and our main finding remains unaffected. However, the omitted
variable problem would only be solved if we measured the effect of economic
output on instability perfectly. Since this is unlikely, we present a different
add two type of analysis. We use a related dataset that offers detailed
information, for a shorter time period, on the causes behind each unrest
event. This allows us to demonstrate the connection between social instability
and expenditure cuts more directly.
       As described in the data section, the EPCD’s dataset allows us to pin
down the main motive behind each public demonstration. We examine if the
public assemblies that are motivated by anti-austerity sentiment – as
determined by the newspaper records in Lexis-Nexis – are significantly
affected by actual changes in fiscal policy. Our approach here is similar to
what has been called the “narrative approach” (C.D. Romer and D.H. Romer
1989). Table 7 gives the results. If we use the same specification as in Table 1
(where we analysed the dataset spanning the period 1919-1999), we find
similar results. Increasing expenditure lowers levels of unrest (column 1). The
key variable driving the relationship between budget balance and instability is
expenditure, not taxes (columns 2 and 3). The results are robust to including
country and year fixed effects. In column 6, we investigate what happens
                                                                                17

when we use all forms of demonstrations, not just those associated with
austerity. The coefficient is small, positive, and insignificant.


Table 7: EPCD data on unrest and austerity – 1980 to 1995




We can strengthen this result further by conducting a placebo test. In Table
8, we use a set of alternative types of unrest, and test if they can be predicted
by the same explanatory variables as in Table 7. Labour disputes and unrest
inspired by the state of the economy are more frequent when budgets are
being cut, but the link is not strong or statistically significant. Peace rallies,
and unrest as a result of education issues, show the opposite sign of the
coefficient on austerity – times of rising expenditure also seem to bring these
issues to the fore. Overall, the placebo test shows that only in the case of anti-
austerity demonstrations is there a strong and significant link with changes in
government expenditure.
                                                                                                      18

Table 8: Placebo tests




Another way to strengthen the argument for a causal link is to examine
budget measures in more detail. Some of the variation in the budget balance
that we have used so far will simply reflect revenue and expenditure changes
that are driven by the economic cycle. A simple way to deal with the problem
is to use Alesina and Ardagna’s (2010) cyclically-adjusted primary budget
balance. In table 9, col. (2), we report the results. The coefficient on budget
changes is almost identical to the baseline specification. In col. (3), we use the
IMF measure of policy-action based changes in the budget balance.11 This also
produces a large, significant coefficient. The closer we get to measuring the
impact of policy measures, the larger coefficient becomes. This strengthens the
case for a causal link between unrest and austerity.




11
  Since Devries et al. (2011) only report positive changes in the budget balance, data from IMF
International Financial Statistics has been used to proxy for negative changes in the budget position in
the IMF (2011) series, sign and size of the coefficient are not affected by this assumption.
                                                                                19

Table 9: Unrest and alternative measures of budget balance




4. Robustness and Extensions
In this section, we examine the sensitivity of our results. We first examine
interaction effects with institutional factors. Do countries with more
accountable governments weather the storms of austerity better?. We also
examine if the effect may be driven by outliers, whether positive or negative
changes in expenditure matter more for the effect on unrest, and whether the
effect is constant in all parts of the distribution of the dependent variable.
        Greater constraints on the executive and more democracy should on
the hand - reduce social conflict; on the other, there will be less repression by
the authorities as Polity scores improve. Which effect dominates is not clear
ex ante. Table 10 demonstrates that in countries with better institutions, the
responsiveness of unrest to budget cuts is generally lower. Where constraints
on the executive are minimal, the coefficient on expenditure changes is
strongly negative – more spending buys a lot of social peace. In countries with
Polity-2 scores above zero, the coefficient is about half in size, and less
significant. As we limit the sample to ever more democratic countries, the size
of the coefficient declines. For full democracies with a complete range of civil
rights, the coefficient is still negative, but no longer significant.
        The link with growth is less clear-cut. Higher output hardly dents the
tendency to riot, demonstrate, assassinate, or strike in countries with low
institutional quality. The opposite is true on average in countries with scores
                                                                               20

above zero, and throughout the range of scores. The only exception is for full
democracies, where the connection is weaker.


Table 10: Unrest and Institutional Quality (dependent variable: CHAOS)




When does the link between budget cuts and unrest become particularly
strong? We examine which part of the distribution of CHAOS shows a
particularly large impact of austerity measures. To do so, we estimate quantile
regressions, where we estimate the conditional median, and then the effect
from the 5th to the 95th percentile of the distribution of CHAOS. Figure 5
shows the size of effects. The estimated coefficient is zero for much of the
range. Only from the 80th percentile upwards – for country-year observations
with two or more incidents – is the effect visible. It then grows rapidly as
estimated coefficient on expenditure changes (and on output growth) increases
at higher and higher percentiles of the distribution of CHAOS. This suggests
that unrest reacts particularly strongly to budget cuts and growth when
unrest levels are already high.
                                                                                             21




      0.00




                                                    0.00
                                                    -5.00
      -10.00




                                                    -10.00
   -20.00
    dexp




                                               dy
                                                    -15.00
      -30.00




                                                    -20.00
      -40.00




                                                    -25.00


               0   .2   .4       .6   .8   1                 0   .2   .4       .6   .8   1
                         Quantile                                      Quantile




Figure 5: Quantile Regression Plot, Expenditure and Growth (95% confidence
intervals)


How much does our main finding depend on the way in which we aggregate
unrest? CHAOS is the simple sum of incidents. Instead, we can use the
weighted conflict index, as compiled by Banks (1994) and collaborators. It
encompasses a larger set of domestic conflicts including, in addition to the
components of CHAOS, purges, major government crisis and guerrilla warfare.
It also assigns different, fixed weights to each individual component. The
correlation coefficient of the variable with CHAOS is 0.75, significant at the
1% level. Another alternative is to use the first principal component of the
five indicators that go into CHAOS. They all enter with a positive weighting.
The first principal component explains 0.42 of the overall variance. The
correlation coefficient with CHAOS is 0.98.
In Table 11, we use both wci and the first principal as dependent variables.
Since the dependent variable is no longer a count variable, we use panel OLS,
and obtain large and significant coefficients for expenditure changes and the
budget position. As before, the same is not true for tax changes. The results
are largely identical in terms of magnitude and significance with the baseline
results in Table 3. We conclude that the way in which we measure unrest does
not matter for our main finding.
                                                                              22


Table 11: Unrest and Budget Cuts – Alternative Indicators of Unrest




An additional factor that can be questioned involves the use of the sum of
unrest in the baseline results. The variable CHAOS is designed to capture the
intensity of unrest, but it may be that it is influenced by a number of outliers
with a high count of incidents. This would then make it easier to find
significant effects. To examine this potential issue, we transform CHAOS into
a simple dichotomous variable, with unrest coded as equal to unity if there are
one or more incidents in a country in a single year. In table 12, we re-estimate
the baseline regression with panel logit using country- and year-fixed effects.
We find the same results as before – expenditure cuts wreak havoc, tax
increases do so only to a small extent and insignificantly. Overall, the budget
balance matters for predicting unrest. We conclude that the role of outliers is
not decisive in underpinning the relationship we established in baseline
results.
                                                                             23



Table 12: CHAOS as a dichotomous variable




Which part of the variation in the explanatory variables is responsible for the
link between austerity and unrest? Do increases in expenditure do as much to
reduce unrest as cuts increase them? In Table 13, we look at the issue.
Column (1) shows the results for expenditure changes that are positive. The
coefficient is negative, but not large, and not significant. In contrast, if
expenditure changes are negative, they matter a great deal for unrest, driving
up CHAOS by 0.19 incidents for each standard deviation of expenditure cuts.
Next, we repeat the exercise for output changes. Increases in output do much
to cut unrest (col. 3), with a one standard deviation increase in output
(3.77%) reducing CHAOS by 0.2 incidents on average. In contrast, declines do
not set off major disruptions to the same degree. Overall, the results in table
12 confirm that the relevant identifying variation for expenditure changes
comes from cuts; for output changes, it comes from positive growth, not
recessions.
                                                                                24

Table 13: Instability, Expenditure Cuts and Growth




Does greater media penetration increase or reduce unrest? Events in the Arab
world in 2010 and early 2011 have led many to believe that greater media
availability tightens the link between discontent and unrest. Data on media
penetration is available in the Banks dataset. Four indicators are suitable –
phone penetrations per capita, radio and television take-up, and the number
of telegrams sent per capita. Radio and television are unidirectional forms of
media, allowing typically government-controlled messages to be broadcast to
the population. If anything, they should make it easier for authorities to
reduce unrest. Phones and telegrams, on the other hand, allow peer-to-peer
communication. All else equal, the expected effect is that they facilitate
organized protest.
        To analyse the data, and to avoid confusing results with the growing
availability of broadcasting and telecommunications over time, we rank
penetration rate in our sample in each year. We do separately for each
category, and then sum the ranks for each country-year. This gives a rank
ordering of media penetration in year y. We then divide the sample at the
median. Table 14, col. (1) and (2) presents the results. We find that below-
average media penetration is associated with a strong effect of expenditure
cuts on unrest. Above the median, the effect disappears. There is also some
evidence that the opposite pattern obtains with respect to economic
conditions – the responsiveness to output changes increases as media
penetration grows. In col. (3)-(6), we differentiate between uni-directional
information media (infomedia) and peer-to-peer telecommunications
(peermedia). While there is some attenuation of the effect of expenditure
                                                                                                    25

changes, it is milder than for all media. For both types, the effect of economic
conditions changes from insignificant (in the part of the sample with below-
median penetration) to highly significant (above the median). These results do
not suggest that countries which, at any one point of time, have greater
availability of mass media (relative to their neighbors) experience a higher
level of unrest.12

Table 14: Media Penetration and Unrest




5. Conclusions
The political economy literature on austerity suggests a paradox. There is no
significant punishment at the polls for governments pursuing cut-backs
(Alesina, Perotti, and Tavares 1998; Alesina, Carloni, and Lecce 2010), and no
evidence of gains in response to budget expansion (Brender and A. Drazen
2008). Also, the empirical evidence on the economic effects of budget cuts is
mixed, with some studies finding an expansionary effect, and others, a
contractionary one.13 Why, then, is fiscal consolidation often delayed, or only
implemented half-heartedly?
      This paper suggests one possible reason why austerity measures are
often avoided – fear of instability and unrest.14 Expenditure cuts carry a
significant risk of increasing the frequency of riots, anti-government


12
   The obvious alternative is to condition on the absolute level of, say, phone penetration. Most of the
variation in phone penetration, however, simply reflects GDP growth and the declining cost of
telephones relative to all other goods; no clear pattern emerges.
13
   Alesina and Silvio Ardagna 2010; Alesina, Silvio Ardagna, et al. 2002; Pescatori, Leigh, and
Guajardo 2011. An early example in the literature is Giavazzi and Pagano (1990).
14
   Alesina, Carloni and Lecce (2010) also suggest that implementation of budget measures may be
harder if the burden falls disproportionately on some groups. War-of-attrition models of consolidation
are one alternative (Alesina and Drazen 1991).
                                                                             26

demonstrations, general strikes, political assassinations, and attempts at
revolutionary overthrow of the established order. While these are low-
probability events in normal years, they become much more common as
austerity measures are implemented. This may act as a potent brake on
governments. In line with our results on expenditure, Woo (2003) showed that
countries with higher levels of unrest are more indebted. High levels of
instability show a particularly clear connection with fiscal consolidation.
        We demonstrate that the general pattern of association between unrest
and budget cuts holds in Europe for the period 1919-2009. It can be found in
almost all sub-periods, and for all types of unrest. Strikingly, where we can
trace the cause of each incident (during the period 1980-95), we can show that
only austerity-inspired demonstrations respond to budget cuts in the time-
series. Also, when we use recently-developed data that allows clean
identification of policy-driven changes in the budget balance, our results hold.
Finally, the results are not affected by using alternative measures of unrest.
Contrary to what might be expected, we also find no evidence that the spread
of mass media facilitates the rise of mass protests.
                                                                            27

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